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Eigen, D., Puhrsch, C. and Fergus, R. (2014) Depth Map Prediction from a Single Image Using a Multi-Scale Deep Network. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D. and Weinberger, K.Q., Eds., Proceedings of the 27th International Conference on Neural Information Processing Systems, MIT Press, Cambridge, MA, 2366-2374.

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• TITLE: Hyper 360—Towards a Unified Tool Set Supporting Next Generation VR Film and TV Productions

  AUTHORS: Barnabas Takacs, Zsuzsanna Vincze, Hannes Fassold, Antonis Karakottas, Nikolaos Zioulis, Dimitrios Zarpalas, Petros Daras

  KEYWORDS: Deep Learning, Tensor Flow, YoloV3, 360˚ Video, Virtual Reality, Free Viewpoint Video, Quality Control, Automatic Cinematography, Multi-Camera Systems

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.12 No.5, May 27, 2019

  ABSTRACT: We describe four fundamental challenges that complex real-life Virtual Reality (VR) productions are facing today (such as multi-camera management, quality control, automatic annotation with cinematography and 360˚depth estimation) and describe an integrated solution, called Hyper 360, to address them. We demonstrate our solution and its evaluation in the context of practical productions and present related results.